code: def nanprod(
      a: paddle.Tensor,
      /,
      *,
      axis: Optional[Union[int, Tuple[int]]] = None,
      keepdims: Optional[bool] = False,
      dtype: Optional[paddle.dtype] = None,
      out: Optional[paddle.Tensor] = None,
      initial: Optional[Union[int, float, complex]] = None,
      where: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      dtype = ivy.as_native_dtype(dtype)
      if dtype is None:
          dtype = _infer_dtype(a.dtype)
      a = a.cast(dtype)
      if initial is None:
          initial = 1
      a = paddle.nan_to_num(a, nan=1.0)
      ret = paddle.prod(a, axis=axis, keepdim=keepdims) * initial
  
      if isinstance(axis, Sequence):
          if len(axis) == a.ndim:
              axis = None
      if (a.ndim == 1 or axis is None) and not keepdims:
          ret = ret.squeeze()
      return ret.cast(dtype)
  
imports: from typing import Optional, Union, Tuple, Sequence, Any
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from copy import deepcopy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes, with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 6 for calling a global function with example usage: nanprod() and returns: ret